Why Jev Is Changing How We Build With AI with Diogo Almeida

Sam Charrington (0:00)

Three weeks ago, TypeSafe came out of stealth with Jev, and it quickly became one of the most talked about new models in AI.

When it was released, developers split into two camps. Some shrugged and said it was just a classifier. Others saw something fast and cheap and easy to build with. Within days, open source lookalikes were showing up, and even OpenAI has since announced its own response. But according to the person who built it, most of this discussion misses the point. Jev's real advance isn't speed or cost, it's a different idea of what AI should be optimized for.

Diogo Almeida is co-founder and CEO of TypeSafe. Before that, he spent four and a half years at OpenAI, where he was part of the team behind InstructGPT and the RLHF work that helped turn language models into the assistants we use today. With Jev, he's now pursuing what he calls machine-native intelligence. Models optimized not for generating strings that people consume, but for making reliable decisions that software can act on. Here's Diogo on the gap he's trying to close.

Diogo Almeida (0:58)

The elevator pitch I use for Jev is just where the f*** is all the automation.

I think AI is just so unbelievably smart, yet so unbelievably useless at the kinds of things you'd really expect it to be useful for. And most people, as far as I can tell, don't really have an answer on why there's like this entire bucket of stuff where AI is not just good, it's like super humanly good, like chat to GPT, deep research, cloud code, and the other bucket of stuff where it's not just bad, it's so bad that it can't even be used at all, despite having enormous financial incentive.

Sam Charrington (1:36)

I'm Sam Charrington and this is the TWIML AI Podcast. For over a decade, I've been exploring the ideas and innovation shaping the future of AI through conversations like this one that help you understand what's real, what's next, and what matters. Let's jump in.

Diogo Almeida (1:58)

Why can't people use agents for basic data entry and insurance underwriting? And actually, if you go to... Oh, man, I don't want to be taking stabs at people. But if you go even to Anthropics website and you try to use their customer support agent to do anything, it's really great at throwing the FAQ at you. But it is not useful if you want to do something even easier like changing a credit card.

And that, I think, is a thing that everyone who is AI adjacent, at least, should have an answer to why that's the case. Kind of curious if you have an answer why there's such a big divide between super smart stuff and super useless stuff.

Sam Charrington (2:40)

I mean, I think part of it comes down to, we've got this one tool in modern AI, right? It's this kind of token predicting transformer at the core with a bunch of machinery around it that makes it, you know, better at producing, you know, producing content, producing words, producing texts.

And we're trying to kind of jam that into every place we can, but that's not always the best thing to, for every problem. And I suspect that that's probably not too far from your viewpoint on this.

Diogo Almeida (3:15)

A little bit, though, there's so much nuance into it, right? Like, for example, how can this token spewing transformer, like, solve millennium price problems in math, but cannot automate accounting, right?

Like, there's a lot of nuance into that, where it really begs some questions that I don't think people in the field are really ready to answer and are very incentivized, at least the AI labs are very incentivized to not answer that truthfully.

Sam Charrington (3:42)

Is the implication there that you are ready to answer that question?

Diogo Almeida (3:45)

Yeah, I could, if you want.

I mean, like, first of all, like, everything is just an opinion, right? So I wouldn't say I have, like, proof of that. I would say I have a lot of conviction and I have a lot of evidence. But the way to prove this would be to actually just automate that job. And I'm hoping that Jev is, like, the first step in that direction. So my take on the answer and, like, the nuance of it is that you get what you optimize for, which is just true of all things ML, kind of true of all things in the world. All things things. All things, yeah. Like, actually, I think ML people get it the least, and people who interact with humans a lot get it the most.